Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
RAG with Natural Language (Semantic) Query (Ex. 5): Fast Start to RAG (2024)
You're really making progress! You are now ready to start semantic searching. Also known as natural language querying, this is where we reap the benefits of embeddings and vector databases. You will be able to query your knowledge base using natural language to ask questions like “what is the executive’s base salary?” to derive answers from complex employment contracts.
Learn the fundamentals of semantic searches in this easy-to-follow example.
Example 5 of 6 Examples – please follow along with the rest of the examples so you can become familiar with all of the basic components of RAG in our detailed but simple tutorials!
Prerequisite: Python, AI-Beginner Friendly
Please subscribe for more content.
Also check us out on our open source library on Github and leave a star! https://githu...
published: 28 Feb 2024
Vector Search: The Future of Data Querying Explained | Semantic Searching
✅ Sign-up for a free cluster at → https://mdb.link/free-1ZIYVNvRVsY
✅ Get help on our Community Forums → https://mdb.link/community-1ZIYVNvRVsY
** Support for the '$vectorSearch' aggregation pipeline stage is available with MongoDB Atlas 6.0.11 and 7.0.2. **
Welcome to this comprehensive guide on Vector Search, a revolutionary technique that allows you to search data based on meaning rather than just keywords. If you've ever struggled to find exactly what you're looking for in a database, this video is for you. We'll explore how Vector Search works, its benefits, and how to implement it in MongoDB for a seamless and powerful search experience.
🛠 What You'll Learn
What is Vector Search?: Understand the machine learning models behind Vector Search and how they transform text, audio, imag...
published: 27 Sep 2023
What is Semantic Search?
This video is part of LLM University
https://docs.cohere.com/docs/what-is-semantic-search
Semantic search is a very effective way to search documents with a query. In this article, you’ll learn how to use embeddings and similarity in order to build a semantic search model.
Bio:
Luis Serrano is the lead of developer relations at Co:here. Previously he has been a research scientist and an educator in machine learning and quantum computing. Luis did his PhD in mathematics at the University of Michigan, before embarking to Silicon Valley to work at several companies like Google and Apple. Luis is the author of the Amazon best-seller "Grokking Machine Learning", where he explains machine learning in a clear and concise way, and he is the creator of the educational YouTube channel "Serrano.Ac...
published: 11 May 2023
Semantic Query Processing for Inclusive Education
published: 06 Dec 2020
Finish the Puzzle: Querying Power BI Semantic Models with Semantic Link!
Want to use notebooks to leverage data from a Power BI semantic model (dataset)? Maybe you're a data scientist wanting to augment your models with that data, or maybe you just want to validate stuff as a Power BI developer. Semantic Link (or Sempy) can help!
Sample Notebook: https://github.com/guyinacube/demo-files/blob/master/video%20demos/Synapse/20231019%20-%20Sempy.ipynb
What is semantic link?
https://learn.microsoft.com/fabric/data-science/semantic-link-overview
Tutorial: Clean data with functional dependencies
https://learn.microsoft.com/fabric/data-science/tutorial-data-cleaning-functional-dependencies
Tutorial: Extract and calculate Power BI measures from a Jupyter notebook
https://learn.microsoft.com/fabric/data-science/tutorial-power-bi-measures
Detect, explore, and validate...
published: 19 Oct 2023
Semantic query application
published: 06 May 2016
Semantic Search: Find What You Mean, Not What You Type!
In this video, we'll take our search game to the next level by using semantic techniques. Instead of relying on literal text matching, we'll search by meaning and context. We'll use natural language processing to improve our search results and find exactly what we're looking for.
⭐ Our startup: https://wordful.ai
⭐ My GitHub: https://github.com/joschan21
The stuff I use to make my videos
Camera: https://amzn.to/3XcqRKO
Light: https://amzn.to/3Xc1yIE
Keyboard: https://amzn.to/3CKxnAi
Mouse: https://amzn.to/3CNcfcm
Microphone: https://amzn.to/3iybVHC
Headphones: https://amzn.to/3IHTTgH
thats pretty much it. Those are affiliate links so I might earn a commission if you purchase after clicking them. :^)
published: 31 Jan 2023
Creating Custom Analytical Queries with Semantic Tags - SAP S/4HANA Cloud Technology Topics
In this video, you will learn how to create a custom analytical query in order to consume semantic tag KPIs.
Creating Custom Analytical Queries with Semantic Tags - SAP S/4HANA Cloud Technology Topics :
*************************
This video also appears in that playlists : https://www.youtube.com/@SAPopen?sub_confirmation=1
published: 24 Feb 2024
Semantic Search - FinBlade AI
Experience effortless data extraction with Semantic Search. Upload your file, ask your query, and instantly retrieve precise information with ease. Our intuitive platform simplifies complex data retrieval, allowing you to quickly gain actionable insights and make informed decisions efficiently.
FinBlade utilizes Qdrant’s high-performance vector databases to deliver superior search capabilities.
Book a demo by visiting https://www.finblade.ai/ today!
#FinbladeAI #AI #Technology #ICSArabia
published: 17 Aug 2024
Semantic Query in Model-Based Definition | Creo 5.0
You can query models per ASME and ISO standards and analyze semantic information related to annotations. Learn more: http://ptc.co/gOKS30jm0QP
(Not sure why there's no audio on this.)
You're really making progress! You are now ready to start semantic searching. Also known as natural language querying, this is where we reap the benefits of emb...
You're really making progress! You are now ready to start semantic searching. Also known as natural language querying, this is where we reap the benefits of embeddings and vector databases. You will be able to query your knowledge base using natural language to ask questions like “what is the executive’s base salary?” to derive answers from complex employment contracts.
Learn the fundamentals of semantic searches in this easy-to-follow example.
Example 5 of 6 Examples – please follow along with the rest of the examples so you can become familiar with all of the basic components of RAG in our detailed but simple tutorials!
Prerequisite: Python, AI-Beginner Friendly
Please subscribe for more content.
Also check us out on our open source library on Github and leave a star! https://github.com/llmware-ai/llmware
Check out our models on Hugging Face: https://huggingface.co/llmware
Discord: https://discord.gg/3N4m5Y5JJH
You're really making progress! You are now ready to start semantic searching. Also known as natural language querying, this is where we reap the benefits of embeddings and vector databases. You will be able to query your knowledge base using natural language to ask questions like “what is the executive’s base salary?” to derive answers from complex employment contracts.
Learn the fundamentals of semantic searches in this easy-to-follow example.
Example 5 of 6 Examples – please follow along with the rest of the examples so you can become familiar with all of the basic components of RAG in our detailed but simple tutorials!
Prerequisite: Python, AI-Beginner Friendly
Please subscribe for more content.
Also check us out on our open source library on Github and leave a star! https://github.com/llmware-ai/llmware
Check out our models on Hugging Face: https://huggingface.co/llmware
Discord: https://discord.gg/3N4m5Y5JJH
✅ Sign-up for a free cluster at → https://mdb.link/free-1ZIYVNvRVsY
✅ Get help on our Community Forums → https://mdb.link/community-1ZIYVNvRVsY
** Support for ...
✅ Sign-up for a free cluster at → https://mdb.link/free-1ZIYVNvRVsY
✅ Get help on our Community Forums → https://mdb.link/community-1ZIYVNvRVsY
** Support for the '$vectorSearch' aggregation pipeline stage is available with MongoDB Atlas 6.0.11 and 7.0.2. **
Welcome to this comprehensive guide on Vector Search, a revolutionary technique that allows you to search data based on meaning rather than just keywords. If you've ever struggled to find exactly what you're looking for in a database, this video is for you. We'll explore how Vector Search works, its benefits, and how to implement it in MongoDB for a seamless and powerful search experience.
🛠 What You'll Learn
What is Vector Search?: Understand the machine learning models behind Vector Search and how they transform text, audio, images, or other types of data into high-dimensional vectors.
Benefits of Vector Search: Discover why semantic understanding, scalability, and flexibility make Vector Search a must-have feature for modern databases.
MongoDB & Vector Search: Learn how to set up Vector Search in MongoDB Atlas, create triggers with OpenAI API, and perform vector search queries. We'll walk you through each step, from setting up your MongoDB Atlas account to writing JavaScript functions for querying.
📚 Resources 📚
✅ Written article & code → https://mdb.link/semantic-search-mongodb-atlas-vector-search-1ZIYVNvRVsY
✅ Vector Search Documentation→ https://mdb.link/vector-search-1ZIYVNvRVsY
✅ OpenAI → https://platform.openai.com/overview
✅ Create your first FREE MongoDB Atlas Cluster → https://youtu.be/jXgJyuBeb_o
------
✅ Subscribe to our channel → https://mdb.link/subscribe
✅ Sign-up for a free cluster at → https://mdb.link/free-1ZIYVNvRVsY
✅ Get help on our Community Forums → https://mdb.link/community-1ZIYVNvRVsY
** Support for the '$vectorSearch' aggregation pipeline stage is available with MongoDB Atlas 6.0.11 and 7.0.2. **
Welcome to this comprehensive guide on Vector Search, a revolutionary technique that allows you to search data based on meaning rather than just keywords. If you've ever struggled to find exactly what you're looking for in a database, this video is for you. We'll explore how Vector Search works, its benefits, and how to implement it in MongoDB for a seamless and powerful search experience.
🛠 What You'll Learn
What is Vector Search?: Understand the machine learning models behind Vector Search and how they transform text, audio, images, or other types of data into high-dimensional vectors.
Benefits of Vector Search: Discover why semantic understanding, scalability, and flexibility make Vector Search a must-have feature for modern databases.
MongoDB & Vector Search: Learn how to set up Vector Search in MongoDB Atlas, create triggers with OpenAI API, and perform vector search queries. We'll walk you through each step, from setting up your MongoDB Atlas account to writing JavaScript functions for querying.
📚 Resources 📚
✅ Written article & code → https://mdb.link/semantic-search-mongodb-atlas-vector-search-1ZIYVNvRVsY
✅ Vector Search Documentation→ https://mdb.link/vector-search-1ZIYVNvRVsY
✅ OpenAI → https://platform.openai.com/overview
✅ Create your first FREE MongoDB Atlas Cluster → https://youtu.be/jXgJyuBeb_o
------
✅ Subscribe to our channel → https://mdb.link/subscribe
This video is part of LLM University
https://docs.cohere.com/docs/what-is-semantic-search
Semantic search is a very effective way to search documents with a qu...
This video is part of LLM University
https://docs.cohere.com/docs/what-is-semantic-search
Semantic search is a very effective way to search documents with a query. In this article, you’ll learn how to use embeddings and similarity in order to build a semantic search model.
Bio:
Luis Serrano is the lead of developer relations at Co:here. Previously he has been a research scientist and an educator in machine learning and quantum computing. Luis did his PhD in mathematics at the University of Michigan, before embarking to Silicon Valley to work at several companies like Google and Apple. Luis is the author of the Amazon best-seller "Grokking Machine Learning", where he explains machine learning in a clear and concise way, and he is the creator of the educational YouTube channel "Serrano.Academy", with over 100K subscribers and 5M views.
===
Resources:
Blog post: https://txt.cohere.com/what-is-semantic-search/
Learn more: https://www.youtube.com/c/LuisSerrano
This video is part of LLM University
https://docs.cohere.com/docs/what-is-semantic-search
Semantic search is a very effective way to search documents with a query. In this article, you’ll learn how to use embeddings and similarity in order to build a semantic search model.
Bio:
Luis Serrano is the lead of developer relations at Co:here. Previously he has been a research scientist and an educator in machine learning and quantum computing. Luis did his PhD in mathematics at the University of Michigan, before embarking to Silicon Valley to work at several companies like Google and Apple. Luis is the author of the Amazon best-seller "Grokking Machine Learning", where he explains machine learning in a clear and concise way, and he is the creator of the educational YouTube channel "Serrano.Academy", with over 100K subscribers and 5M views.
===
Resources:
Blog post: https://txt.cohere.com/what-is-semantic-search/
Learn more: https://www.youtube.com/c/LuisSerrano
Want to use notebooks to leverage data from a Power BI semantic model (dataset)? Maybe you're a data scientist wanting to augment your models with that data, or...
Want to use notebooks to leverage data from a Power BI semantic model (dataset)? Maybe you're a data scientist wanting to augment your models with that data, or maybe you just want to validate stuff as a Power BI developer. Semantic Link (or Sempy) can help!
Sample Notebook: https://github.com/guyinacube/demo-files/blob/master/video%20demos/Synapse/20231019%20-%20Sempy.ipynb
What is semantic link?
https://learn.microsoft.com/fabric/data-science/semantic-link-overview
Tutorial: Clean data with functional dependencies
https://learn.microsoft.com/fabric/data-science/tutorial-data-cleaning-functional-dependencies
Tutorial: Extract and calculate Power BI measures from a Jupyter notebook
https://learn.microsoft.com/fabric/data-science/tutorial-power-bi-measures
Detect, explore, and validate functional dependencies in your data
https://learn.microsoft.com/fabric/data-science/semantic-link-validate-data
fabric Package
https://learn.microsoft.com/python/api/semantic-link-sempy/sempy.fabric?view=semantic-link-python
📢 Become a member: https://guyinacu.be/membership
*******************
Want to take your Power BI skills to the next level? We have training courses available to help you with your journey.
🎓 Guy in a Cube courses: https://guyinacu.be/courses
*******************
LET'S CONNECT!
*******************
-- http://twitter.com/guyinacube
-- http://twitter.com/awsaxton
-- http://twitter.com/patrickdba
-- http://www.facebook.com/guyinacube
-- https://www.instagram.com/guyinacube/
-- https://guyinacube.com
***Gear***
🛠 Check out my Tools page - https://guyinacube.com/tools/
#MicrosoftFabric #PowerBI #GuyInACube
Want to use notebooks to leverage data from a Power BI semantic model (dataset)? Maybe you're a data scientist wanting to augment your models with that data, or maybe you just want to validate stuff as a Power BI developer. Semantic Link (or Sempy) can help!
Sample Notebook: https://github.com/guyinacube/demo-files/blob/master/video%20demos/Synapse/20231019%20-%20Sempy.ipynb
What is semantic link?
https://learn.microsoft.com/fabric/data-science/semantic-link-overview
Tutorial: Clean data with functional dependencies
https://learn.microsoft.com/fabric/data-science/tutorial-data-cleaning-functional-dependencies
Tutorial: Extract and calculate Power BI measures from a Jupyter notebook
https://learn.microsoft.com/fabric/data-science/tutorial-power-bi-measures
Detect, explore, and validate functional dependencies in your data
https://learn.microsoft.com/fabric/data-science/semantic-link-validate-data
fabric Package
https://learn.microsoft.com/python/api/semantic-link-sempy/sempy.fabric?view=semantic-link-python
📢 Become a member: https://guyinacu.be/membership
*******************
Want to take your Power BI skills to the next level? We have training courses available to help you with your journey.
🎓 Guy in a Cube courses: https://guyinacu.be/courses
*******************
LET'S CONNECT!
*******************
-- http://twitter.com/guyinacube
-- http://twitter.com/awsaxton
-- http://twitter.com/patrickdba
-- http://www.facebook.com/guyinacube
-- https://www.instagram.com/guyinacube/
-- https://guyinacube.com
***Gear***
🛠 Check out my Tools page - https://guyinacube.com/tools/
#MicrosoftFabric #PowerBI #GuyInACube
In this video, we'll take our search game to the next level by using semantic techniques. Instead of relying on literal text matching, we'll search by meaning a...
In this video, we'll take our search game to the next level by using semantic techniques. Instead of relying on literal text matching, we'll search by meaning and context. We'll use natural language processing to improve our search results and find exactly what we're looking for.
⭐ Our startup: https://wordful.ai
⭐ My GitHub: https://github.com/joschan21
The stuff I use to make my videos
Camera: https://amzn.to/3XcqRKO
Light: https://amzn.to/3Xc1yIE
Keyboard: https://amzn.to/3CKxnAi
Mouse: https://amzn.to/3CNcfcm
Microphone: https://amzn.to/3iybVHC
Headphones: https://amzn.to/3IHTTgH
thats pretty much it. Those are affiliate links so I might earn a commission if you purchase after clicking them. :^)
In this video, we'll take our search game to the next level by using semantic techniques. Instead of relying on literal text matching, we'll search by meaning and context. We'll use natural language processing to improve our search results and find exactly what we're looking for.
⭐ Our startup: https://wordful.ai
⭐ My GitHub: https://github.com/joschan21
The stuff I use to make my videos
Camera: https://amzn.to/3XcqRKO
Light: https://amzn.to/3Xc1yIE
Keyboard: https://amzn.to/3CKxnAi
Mouse: https://amzn.to/3CNcfcm
Microphone: https://amzn.to/3iybVHC
Headphones: https://amzn.to/3IHTTgH
thats pretty much it. Those are affiliate links so I might earn a commission if you purchase after clicking them. :^)
In this video, you will learn how to create a custom analytical query in order to consume semantic tag KPIs.
Creating Custom Analytical Queries with Semantic...
In this video, you will learn how to create a custom analytical query in order to consume semantic tag KPIs.
Creating Custom Analytical Queries with Semantic Tags - SAP S/4HANA Cloud Technology Topics :
*************************
This video also appears in that playlists : https://www.youtube.com/@SAPopen?sub_confirmation=1
In this video, you will learn how to create a custom analytical query in order to consume semantic tag KPIs.
Creating Custom Analytical Queries with Semantic Tags - SAP S/4HANA Cloud Technology Topics :
*************************
This video also appears in that playlists : https://www.youtube.com/@SAPopen?sub_confirmation=1
Experience effortless data extraction with Semantic Search. Upload your file, ask your query, and instantly retrieve precise information with ease. Our intuitiv...
Experience effortless data extraction with Semantic Search. Upload your file, ask your query, and instantly retrieve precise information with ease. Our intuitive platform simplifies complex data retrieval, allowing you to quickly gain actionable insights and make informed decisions efficiently.
FinBlade utilizes Qdrant’s high-performance vector databases to deliver superior search capabilities.
Book a demo by visiting https://www.finblade.ai/ today!
#FinbladeAI #AI #Technology #ICSArabia
Experience effortless data extraction with Semantic Search. Upload your file, ask your query, and instantly retrieve precise information with ease. Our intuitive platform simplifies complex data retrieval, allowing you to quickly gain actionable insights and make informed decisions efficiently.
FinBlade utilizes Qdrant’s high-performance vector databases to deliver superior search capabilities.
Book a demo by visiting https://www.finblade.ai/ today!
#FinbladeAI #AI #Technology #ICSArabia
You can query models per ASME and ISO standards and analyze semantic information related to annotations. Learn more: http://ptc.co/gOKS30jm0QP
(Not sure why...
You can query models per ASME and ISO standards and analyze semantic information related to annotations. Learn more: http://ptc.co/gOKS30jm0QP
(Not sure why there's no audio on this.)
You can query models per ASME and ISO standards and analyze semantic information related to annotations. Learn more: http://ptc.co/gOKS30jm0QP
(Not sure why there's no audio on this.)
You're really making progress! You are now ready to start semantic searching. Also known as natural language querying, this is where we reap the benefits of embeddings and vector databases. You will be able to query your knowledge base using natural language to ask questions like “what is the executive’s base salary?” to derive answers from complex employment contracts.
Learn the fundamentals of semantic searches in this easy-to-follow example.
Example 5 of 6 Examples – please follow along with the rest of the examples so you can become familiar with all of the basic components of RAG in our detailed but simple tutorials!
Prerequisite: Python, AI-Beginner Friendly
Please subscribe for more content.
Also check us out on our open source library on Github and leave a star! https://github.com/llmware-ai/llmware
Check out our models on Hugging Face: https://huggingface.co/llmware
Discord: https://discord.gg/3N4m5Y5JJH
✅ Sign-up for a free cluster at → https://mdb.link/free-1ZIYVNvRVsY
✅ Get help on our Community Forums → https://mdb.link/community-1ZIYVNvRVsY
** Support for the '$vectorSearch' aggregation pipeline stage is available with MongoDB Atlas 6.0.11 and 7.0.2. **
Welcome to this comprehensive guide on Vector Search, a revolutionary technique that allows you to search data based on meaning rather than just keywords. If you've ever struggled to find exactly what you're looking for in a database, this video is for you. We'll explore how Vector Search works, its benefits, and how to implement it in MongoDB for a seamless and powerful search experience.
🛠 What You'll Learn
What is Vector Search?: Understand the machine learning models behind Vector Search and how they transform text, audio, images, or other types of data into high-dimensional vectors.
Benefits of Vector Search: Discover why semantic understanding, scalability, and flexibility make Vector Search a must-have feature for modern databases.
MongoDB & Vector Search: Learn how to set up Vector Search in MongoDB Atlas, create triggers with OpenAI API, and perform vector search queries. We'll walk you through each step, from setting up your MongoDB Atlas account to writing JavaScript functions for querying.
📚 Resources 📚
✅ Written article & code → https://mdb.link/semantic-search-mongodb-atlas-vector-search-1ZIYVNvRVsY
✅ Vector Search Documentation→ https://mdb.link/vector-search-1ZIYVNvRVsY
✅ OpenAI → https://platform.openai.com/overview
✅ Create your first FREE MongoDB Atlas Cluster → https://youtu.be/jXgJyuBeb_o
------
✅ Subscribe to our channel → https://mdb.link/subscribe
This video is part of LLM University
https://docs.cohere.com/docs/what-is-semantic-search
Semantic search is a very effective way to search documents with a query. In this article, you’ll learn how to use embeddings and similarity in order to build a semantic search model.
Bio:
Luis Serrano is the lead of developer relations at Co:here. Previously he has been a research scientist and an educator in machine learning and quantum computing. Luis did his PhD in mathematics at the University of Michigan, before embarking to Silicon Valley to work at several companies like Google and Apple. Luis is the author of the Amazon best-seller "Grokking Machine Learning", where he explains machine learning in a clear and concise way, and he is the creator of the educational YouTube channel "Serrano.Academy", with over 100K subscribers and 5M views.
===
Resources:
Blog post: https://txt.cohere.com/what-is-semantic-search/
Learn more: https://www.youtube.com/c/LuisSerrano
Want to use notebooks to leverage data from a Power BI semantic model (dataset)? Maybe you're a data scientist wanting to augment your models with that data, or maybe you just want to validate stuff as a Power BI developer. Semantic Link (or Sempy) can help!
Sample Notebook: https://github.com/guyinacube/demo-files/blob/master/video%20demos/Synapse/20231019%20-%20Sempy.ipynb
What is semantic link?
https://learn.microsoft.com/fabric/data-science/semantic-link-overview
Tutorial: Clean data with functional dependencies
https://learn.microsoft.com/fabric/data-science/tutorial-data-cleaning-functional-dependencies
Tutorial: Extract and calculate Power BI measures from a Jupyter notebook
https://learn.microsoft.com/fabric/data-science/tutorial-power-bi-measures
Detect, explore, and validate functional dependencies in your data
https://learn.microsoft.com/fabric/data-science/semantic-link-validate-data
fabric Package
https://learn.microsoft.com/python/api/semantic-link-sempy/sempy.fabric?view=semantic-link-python
📢 Become a member: https://guyinacu.be/membership
*******************
Want to take your Power BI skills to the next level? We have training courses available to help you with your journey.
🎓 Guy in a Cube courses: https://guyinacu.be/courses
*******************
LET'S CONNECT!
*******************
-- http://twitter.com/guyinacube
-- http://twitter.com/awsaxton
-- http://twitter.com/patrickdba
-- http://www.facebook.com/guyinacube
-- https://www.instagram.com/guyinacube/
-- https://guyinacube.com
***Gear***
🛠 Check out my Tools page - https://guyinacube.com/tools/
#MicrosoftFabric #PowerBI #GuyInACube
In this video, we'll take our search game to the next level by using semantic techniques. Instead of relying on literal text matching, we'll search by meaning and context. We'll use natural language processing to improve our search results and find exactly what we're looking for.
⭐ Our startup: https://wordful.ai
⭐ My GitHub: https://github.com/joschan21
The stuff I use to make my videos
Camera: https://amzn.to/3XcqRKO
Light: https://amzn.to/3Xc1yIE
Keyboard: https://amzn.to/3CKxnAi
Mouse: https://amzn.to/3CNcfcm
Microphone: https://amzn.to/3iybVHC
Headphones: https://amzn.to/3IHTTgH
thats pretty much it. Those are affiliate links so I might earn a commission if you purchase after clicking them. :^)
In this video, you will learn how to create a custom analytical query in order to consume semantic tag KPIs.
Creating Custom Analytical Queries with Semantic Tags - SAP S/4HANA Cloud Technology Topics :
*************************
This video also appears in that playlists : https://www.youtube.com/@SAPopen?sub_confirmation=1
Experience effortless data extraction with Semantic Search. Upload your file, ask your query, and instantly retrieve precise information with ease. Our intuitive platform simplifies complex data retrieval, allowing you to quickly gain actionable insights and make informed decisions efficiently.
FinBlade utilizes Qdrant’s high-performance vector databases to deliver superior search capabilities.
Book a demo by visiting https://www.finblade.ai/ today!
#FinbladeAI #AI #Technology #ICSArabia
You can query models per ASME and ISO standards and analyze semantic information related to annotations. Learn more: http://ptc.co/gOKS30jm0QP
(Not sure why there's no audio on this.)
Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
Generative AI is working well in code generation—generating SQL queries and creating natural language interfaces for querying data ... For instance, Postgres has a vector database add-in, which is useful for retrieval-augmented generation (RAG) queries.
Unpacking Semantic Search Techniques While schema markup addresses how content is presented to search engines, semantic search focuses on understanding the meaning and intent behind search queries.
Key techniques include proper HTML structure, semantic markup, and mobile responsiveness ...Search engines favor comprehensive coverage of topics, making strategic use of primary and semantic keywords crucial.
Sluggish performance almost always spurs business users to extract and load data into their preferred analytics platform for easier manipulation and faster queries, leading to further semantic spread within localized semantic layers.
... and flexible platform for building sophisticated AI-driven applications that leverage both the semantic understanding of vector embeddings and the precise matching of traditional database queries.
Generative AI depends on data to build responses to user queries ...RAG creates a set of data that can be searched for relevant semantic matches to a user query, and those matches are then shared with the LLM for inclusion in the response.
... to expand RAG apps to create agents (what Microsoft calls copilots), as well as for question answering, information extraction, semantic search, and building applications that resolve complex queries.
The crucial importance of the semantic layer ... In companies where multiple data storage systems and various querying and visualization tools are used, the semantic layer provides a unified framework.
... you’re trying to query ... Vector databases provide fast similarity and semantic search while allowing users to find vectors that are the closest to a given query vector based on some distance metric.
Semantic Kernel is the glue, the orchestration layer, that connects LLMs with data and code ... Semantic Kernel AI Orchestration Layer ... It uses Bing search in conjunction with the Semantic Kernel and an OpenAI model to provide current results for queries.
Apple's latest operating system, iOS 18, is set to revolutionize the user experience for iPhone users in India...AppleIntelligence ... It also enhances search with semantic capabilities, allowing for more natural language queries to find photos ... Related ... .
Then, to demonstrate the power of the semantic approach, they apply simple semantic queries to the generated phenotypic descriptions ... As a result, data that has become part of this graph can be queried ...
By expanding and refining user queries based on semantic linkages, synonyms, and related concepts, insight engines enable users to find pertinent insights and conduct more efficient data exploration.
AI-aided search in e-commerce not only does away with the conventional keyword matching strategy but explores more advanced ways like semantic search but with the objective of comprehending and processing the shopper query more intelligently.
(The T in ChatGPT, stands for Transformer) ... (HT PHOTO) ... How did you start working on the Transformers model? At Google, I joined a team of PhDs to build models that look for two queries similar to each other – a concept called semantic similarity ... ....